Prototype selection

A list of paper related to instance selection and construction, metric learning and sparse kernel methods.

This list also includes papers written by us

Total numer of publications: 95

submitted (2)
  • K. Grudzinski E$k$P: A Fast Minimization–Based Prototype Selection Algorithm. Control and Cybernetics submitted
  • K. Grudzinski Selection of Prototypes with the E$k$P System. Control and Cybernetics submitted
2012 (5)
  • M. Blachnik, M. Kordos Extraction of prototype-based threshold rules using neural training procedure. LNCS 7553 pp. 255–262. 2012
  • T. Maszczyk, W. Duch, M. Blachnik Feature ranking methods used for selection of prototypes. LNCS 7553 2012
  • M. Kordos, M. Blachnik Instance Selection with Neural Networks for Regression Problems. LNCS 7553 pp. 263–270. 2012
  • M. Blachnik, M. Kordos Computational Complexity Reduction and Interpretability Improvement of Distance-based Decision Trees.. LNCS 7208 pp. 288-297. 2012
  • M. Blachnik, M. Kordos, T. Wieczorek, S. Golak Selecting Representative Prototypes for Prediction the Oxygen Activity in Electric Arc Furnace. LNCS 2012
2011 (3)
  • M. Blachnik, W. Duch LVQ algorithm with instance weighting for generation of prototype-based rules.. Neural Networks Elsevir. 2011
  • M. Blachnik, M. Kordos Simplnifying SVM with Weighted LVQ Algorithm. LNCS 6936 pp. 212-219. 2011
  • M. Blachnik, M. Kordos Instance Selection and Prototype Based Rules. A new extension to RapidMiner. In Proceedings of RCoMM. 2011
2010 (3)
  • M. Kordos, D. Strzempa, M. Blachnik Do We Need Whatever More than k-NN?. LNCS 6113 pp. 414-421. 2010
  • M. Blachnik, W. Duch Improving Accuracy of LVQ Algorithm by Instance Weighting. LNCS 6354 pp. 257-266. 2010
  • Garcia Salvador, Derrac Joaquin, Cano Jose Ramon, Herrera Francisco Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 34 (3) pp. 417-435. 2010
2009 (3)
  • R. Min, D.A. Stanley, Z. Yuan, A. Bonner, Z. Zhang A deep non-linear feature mapping for large-margin kNN classification. In Data Mining, 2009. ICDM'09. Ninth IEEE International Conference on. pp. 357–366. 2009
  • M. Blachnik Comparison of Various Feature Selection Methods in Application to Prototype Best Rules. Advances in Intelligent and Soft Computing 57 pp. 257-264. Springer Verlag. 2009
  • K.Q. Weinberger, L.K. Saul Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10 pp. 207–244. JMLR. org. 2009
2008 (5)
  • M. Blachnik, J. Laksonen Image Classification by Histogram Features Created With Learning Vector Quantization. LNCS 5163 2008
  • M. Blachnik, W. Duch Rule Extraction from Support Vector Machines. Springer. 2008
  • M. Blachnik, W. Duch Building Localized Basis Function Networks Using Context Dependent Clustering. LNCS 5163 2008
  • K.Q. Weinberger, L.K. Saul Fast solvers and efficient implementations for distance metric learning. In Proceedings of the 25th international conference on Machine learning. pp. 1160–1167. 2008
  • K. Schmidt, T. Behrens, T. Scholten Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma 146 (1) pp. 138–146. Elsevier. 2008
2007 (2)
  • Gan Guojun, Ma Chaoqun, Wu Jianhong Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM. 2007
  • J. Antonia Jones, D. Evans, S.E. Kemp A note on the Gamma test analysis of noisy input output data and noisy time series. Physica D: Nonlinear Phenomena 229 (1) pp. 1-8. 2007
2006 (8)
  • E. P{ k{e}}kalska, R.P.W. Duin, P. Pacl{ ' i}k Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39 (2) pp. 189–208. Elsevier. 2006
  • H. Zhang, A.C. Berg, M. Maire, J. Malik SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. pp. 2126–2136. 2006
  • M. Blachnik, W. Duch Prototype-based threshold rules. LNCS 4234 Physica Verlag, Springer. 2006
  • M. Blachnik, W. Duch, T. Wieczorek . LNCS 4029 pp. 573–582. Physica Verlag, Springer. 2006
  • T. Wieczorek, M. Blachnik, W. Duch Heterogeneous distance functions for prototype rules: influence of parameters on probability estimation. International Journal of Artificial Intelligence Studies 1 pp. xxx–yyy. 2006
  • K. Grudzinski Challenging Problems of Science: Computer Science. pp. 237–244. EXIT, Warsaw. 2006
  • M. Wu, B. Sch{ö}lkopf, G. Bakur A Direct Method for Building Sparse Kernel Learning. The JMLR 4 pp. 603–624. 2006
  • K.Q. Weinberger, J. Blitzer, L.K. Saul Distance metric learning for large margin nearest neighbor classification. In In NIPS. 2006
2005 (7)
  • M. Bereta Hybrid immune algorithm for feature selection and classification of ECG signals. In Methods of Artificial Intelligence. AI-METH Series. Gliwice. 2005
  • T. Wieczorek, M. Blachnik, W. Duch Influence of probability estimation parameters on stability of accuracy in prototype rules using heterogeneous distance functions. Artificial Intelligence Studies 2 pp. 71-78. 2005
  • M. Blachnik, W. Duch, T. Wieczorek Probabilistic distance measures for prototype based rules. In Proc. of ICONIP. pp. 445-450. Taiwan. 2005
  • M. Blachnik, W. Duch, T. Wieczorek Threshold rules decision list. In Methods of artificial intelligence. pp. 23–24. AI-METH Series. Gliwice. 2005
  • R.E. Fan, P.H. Chen, C.J. Lin Working set selection using the second order information for training SVM. JMLR 6 pp. 1889-1918. 2005
  • T. Wieczorek, M. Blachnik, W. Duch Influence of probability estimation parameters on stability of accuracy in prototype rules using heterogeneous distance functions. In Proceedings of Artificial Intelligence Studies. pp. Vol.2. Siedlce. 2005
  • D. Evans Estimating the variance of multiplicative noise. In Noise and Fluctuations: 18th International Conference on Noise and Fluctuations - ICNF 2005. pp. 99-102. 2005
2004 (4)
  • K. Grudzinski SBL–PM-M: A System for Partial Memory Learning. Lecture Notes in Computer Science 3070 pp. 586–591. 2004
  • W. Duch, M. Blachnik Fuzzy rule-based systems derived from similarity to prototypes. In LNCS. pp. 912–917. Physica Verlag, Springer. New York. 2004
  • N. Jankowski, M. Grochowski Comparison of Instance Selection Algorithms. I. Algorithms Survey. Lecture Notes in Computer Science 3070 pp. 598–603. 2004
  • M. Grochowski, N. Jankowski Comparison of Instance Selection Algorithms. II. Results and Comments. LNCS 3070 pp. 580–585. 2004
2003 (4)
  • K. Lin, C. Lin A study on reduced support vector machines. IEEE Transactions on Neural Networks 14 (6) pp. 1449–1459. 2003
  • J.T. Kwok, I.W. Tsang The pre-image problem in kernel methods. IEEE Transactions on Neural Networks 15 pp. 408–415. 2003
  • J.R. Cano, F. Herrera, M. Lozano Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. Evolutionary Computation, IEEE Transactions on 7 (6) pp. 561–575. IEEE. 2003
  • C. Zeng, C.X. Xing, L.Z. Zhou Similarity measure and instance selection for collaborative filtering. In Proceedings of the 12th international conference on World Wide Web. pp. 652–658. 2003
2002 (5)
  • T. Joachims Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publisher. 2002
  • K. Gr{ c a}bczewski, W. Duch Heterogenous forests of decision trees. Springer Lecture Notes in Computer Science 2415 pp. 504–509. Physica Verlag, Springer. 2002
  • Learning with Kernels.. MIT Press, Cambridge. 2002
  • H. Brighton, C. Mellish Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery 6 (2) pp. 153–172. Springer. 2002
  • H. Liu, H. Motoda On issues of instance selection. Data Mining and Knowledge Discovery 6 (2) pp. 115–130. Springer. 2002
2001 (8)
  • H. Liu, H. Motoda Instance selection and construction for data mining. Springer. 2001
  • W. Duch, K. Grudzi 'nski Prototype based rules - new way to understand the data. In IEEE International Joint Conference on Neural Networks. pp. 1858–1863. IEEE Press. Washington D.C. 2001
  • W. Duch, K. Grudzi 'nski Prototype based rules - new way to understand the data. In IEEE International Joint Conference on Neural Networks. pp. 1858–1863. IEEE Press. Washington D.C. 2001
  • Ordered weighted generalized conditional possibilistic clustering. In Zbiory rozmyte i ich zastosowania. pp. 469–
  • R. Collobert, S Bengio {SVMTorch}: Support Vector Machines for Large-Scale Regression Problems. JMLR 1 pp. 143–160. 2001
  • T. Downs, K. Gates, A. Masters Exact Simplification of Support Vector Solutions. The JMLR 2 pp. 293–297. 2001
  • T. Kohonen Self-Organizing Maps. Springer Verlag. 2001
  • F. Schwenker, H. Kestler, G. Palm Three learning phases for radial-basis-function networks. Neural Networks 14 pp. 439-458. 2001
2000 (7)
  • D.R. Wilson, T.R. Martinez Reduction techniques for instance-based learning algorithms. ML 38 pp. 257-268. 2000
  • M. Tipping The relevance vector machine. In Advances in Neural Information Processing Systems. Morgan Kaufmann. 2000
  • L.I. Kuncheva Fuzzy Classifier Design. Physica-Verlag. 2000
  • A new generalized weighted conditional fuzzy clustering. BUSEFAL 81 pp. 8–16. 2000
  • N. Jankowski Ontogeniczne Sieci Neuronowe. In Sieci Neuronowe. 2000
  • W. Duch, R. Adamczak, G.H.F. Diercksen Classification, Association and Pattern Completion using Neural Similarity Based Methods. Applied Mathematics and Computer Science 10 pp. 101–120. 2000
  • S. Bermejo Learning with nearest neighbor classifiers. 2000
1999 (6)
  • J. Platt Using Sparseness and Analytic QP to Speed Training of Support Vector Machines. Advances in Neural Information Processing Systems 11 MIT Press. 1999
  • L. Kuncheva, J.C. Bezdek Presupervised and postsupervised prototype classifier design. IEEE Transactions on Neural Networks 10 (5) pp. 1142-1152. 1999
  • W. Duch, R. Adamczak, G.H.F. Diercksen Distance-based multilayer perceptrons. In International Conference on Computational Intelligence for Modelling Control and Automation. pp. 75–80. IOS Press. Amsterdam, The Netherlands. 1999
  • N. Jankowski Flexible Transfer Functions with Ontogenic Neural. Computational Intelligence Lab, DCM NCU. Toru 'n, Poland. 1999
  • Xuhui Shao, V. Cherkassky Multi-resolution support vector machine. In International Joint Conference on Neural Networks. 1999
  • F. Hoppner, F. Klawonn, R. Kruse, T. Runkler Fuzzy Cluster Analysis. Wiley. 1999
1998 (6)
  • B. Sch{ö}lkopf, P. Knirsch, A. Smola, C. Burges Fast approximation of support vector kernel expansions. Informatik Aktuell, Mustererkennung 1998
  • W. Pedrycz Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks. IEEE Transactions on Neural Networks 9 (4) IEEE Press. 1998
  • L. Kuncheva, J.C. Bezdek Nearest prototype classification: Clustering, genetic algorithms or random search?. IEEE Transactions on Systems, Man, and Cybernetics C28 (1) pp. 160-164. 1998
  • L.I. Kuncheva, J.C. Bezdek An Integrated Framework for Generalized Nearest Prototype Classifier Design. International Journal of Uncertainty 6 (5) pp. 437-457. 1998
  • W. Pedrycz Fuzzy set technology in knowledge discover. Fuzzy Sets and Systems 98 (3) pp. 279-290. 1998
  • L.I. Kuncheva, J.C. Bezdek Nearest prototype classification: Clustering, genetic algorithms, or random search?. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 28 (1) pp. 160–164. IEEE. 1998
1997 (4)
  • N. Jankowski, V. Kadirkamanathan Statistical Control of Growing and Pruning in {RBF}-like Neural Networks. In Third Conference on Neural Networks and Their Applications. pp. 663–670. Kule, Poland. oct 1997
  • N. Jankowski, V. Kadirkamanathan Statistical Control of {RBF}-like Networks for Classification. In 7th International Conference on Artificial Neural Networks. pp. 385–390. Springer-Verlag. Lausanne, Switzerland. oct 1997
  • D.B. Skalak Prototype selection for composite nearest neighbor classifiers. 1997
  • JS S{á}nchez, F. Pla, FJ Ferri Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognition Letters 18 (6) pp. 507–513. Elsevier. 1997
1996 (3)
  • W. Pedrycz Conditional Fuzzy C-Means. Pattern Recognition Letters 17 pp. 625-632. 1996
  • C. Burges Simplified Support Vector Decision Rules. In ICML. pp. 71–77. 1996
  • R. Jang, C.T. Sun Functional Equivalence between Radial Basis Functions Networks and Fuzzy Inference Systems. IEEE Transactions on Neural Networks 1996
1995 (2)
  • R. Cameron-Jones Instance Selection by Encoding Length Heuristic with Random Mutation Hill Climbing. In Proc. of the Eighth Australian Joint Conference on Artificial Intelligence pp. 99-106. 1995
  • W. Duch, G.H.F. Diercksen Feature Space Mapping as a Universal Adaptive System. Computer Physics Communications 87 pp. 341–371. 1995
1994 (2)
  • D.B. Skalak Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Proceedings of the eleventh international conference on machine learning. pp. 293–301. 1994
  • M. Schwabacher, H. Hirsh, T. Ellman Learning prototype-selection rules for case-based iterative design. In Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on. pp. 56–62. 1994
1991 (1)
  • D. Aha, D. Kibler, M.K. Albert Instance-Based Learning Algorithms. Machine Learning 6 pp. 37-66. Kluwer Academic Publishers. 1991
1986 (1)
  • C. Stanfill, D. Waltz Toward memory-based reasoning. Communications of the ACM 29 (12) pp. 1213-1228. 1986
1976 (1)
  • I. Tomek An experiment with the edited nearest-neighbor rule.. IEEE Trans. on Systems, Man, and Cybernetics 6 pp. 448-452. 1976
1974 (1)
  • Chang Chin-Liang Finding Prototypes for Nearest Neighbor Classifiers.. IEEE Transactions on Computers 23 (11) pp. 1179-1184. 1974
1972 (1)
  • D.L. Wilson Assymptotic properties of nearest neighbour rules using edited data.. IEEE Trans. on Systems, Man, and Cybernetics SMC-2 pp. 408-421. 1972
1968 (1)
  • P.E. Hart The condensed nearest neighbor rule.. IEEE Trans. on Information Theory 16 pp. 515-516. 1968
research/protosel.txt · Last modified: 2014/05/19 17:57 (external edit)